Businesses provide services that require a large amount of computing resources at some point in the development or provision of the service. For example, a computer animation company may distribute rendering processing load to a number of computers to produce animations quickly. In another example, an online sales company may distribute incoming requests to a large number of computers acting as web servers to handle a larger traffic load than can be handled by a single computer. Typically, when a business utilizes a large number of computers for load distribution solutions, the computers are stored on racks in a data center. A conventional data center is a room, a floor, or sometimes even an entire building dedicated to housing computing systems configured to perform specific tasks.
One specific concern when designing a data center is heat management. As computers give off heat while operating, a data center may become hot if many computers are operating at the same time. Too much heat can lead to premature system failure. This may create undesirable costs for businesses including loss of revenue due to downtime and increased system repair expenses. Conventional data center thermal monitoring systems may utilize a sparse set of sensors spread throughout a data center. However, typical systems are often too sparse to accurately track heat data in specific areas of a data center. Thus, conventional systems may rely on exception reporting from the machines themselves to determine when heat is exceeding a prescribed threshold. However, at this point, it may be too late to take steps to prevent system failure and incur an undesirable cost. One reason why a data center may have a sparse array of sensors may be due to the difficulty in visually analyzing a large number of sensors in a data center simultaneously over a large period of time. See, for example, FIG. 9. FIG. 9 illustrates how a line graph 900 that tracks a large number of sensors may make it difficult to determine relationships between related sensors.